A significant step in the computer-aided diagnosis of mammary cancer is identifying the suspected masses. The sample data is used for training the classifier before the cases to be measured are being identified. There...
详细信息
A significant step in the computer-aided diagnosis of mammary cancer is identifying the suspected masses. The sample data is used for training the classifier before the cases to be measured are being identified. Therefore, the design of classifier greatly affects the overall performance of the system. Since the number of samples is commonly limited, we extract high-precision feature values to better identify the suspected masses. A nonlinear classifier SVM is introduced into this system, and it is compared with Fisher in classifying effects by carrying out a series of experiments. The Receiver Operating Characteristic Curve (ROC) shows that Az value has reached up to *** experiment results prove that the application of this classifier in identifying and classifying suspected breast lumps helps improve the overall performance of the computer-aided diagnosis system.
Segmentation of colorectal cancerous regions from 3D Magnetic Resonance (MR) images is a crucial procedure for radiotherapy which conventionally requires accurate delineation of tumour boundaries at an expense of labo...
详细信息
We propose an algorithm which uses an optical time-domain reflectometer (OTDR) for real-time tracking of trains. OTDR sensing, often also termed distributed acoustical sensing (DAS), measures the Rayleigh backscatteri...
详细信息
ISBN:
(纸本)9781509015566
We propose an algorithm which uses an optical time-domain reflectometer (OTDR) for real-time tracking of trains. OTDR sensing, often also termed distributed acoustical sensing (DAS), measures the Rayleigh backscattering of a light pulse along an optical fiber. The resulting signal provides information on local acoustic pressure at linearly spaced positions along the fiber. While different approaches for train tracking with DAS are described in the literature, the results have been evaluated only for short time recordings with few train crossings. In this paper we provide details on the tracking performance of a novel algorithm that finds and tracks trains over 15km. Furthermore, this is the first contribution that uses ground truth data to assess the performance of the method. For the evaluation two one hour recordings are used.
Consider a point-set coming from an object which was sampled using a digital sensor (depth range, camera, etc). We are interested in finding a graph that would represent that point-set according to some properties. Su...
详细信息
Radiomics aims to extract and analyze large numbers of quantitative features from medical images and is highly promising in staging, diagnosing, and predicting outcomes of cancer treatments. Nevertheless, several chal...
详细信息
In this paper we address the issue of enhancing salient object detection through diffusion-based techniques. For reliably diffusing the energy from labeled seeds, we propose a novel graph-based diffusion scheme called...
详细信息
ISBN:
(纸本)9781479999897
In this paper we address the issue of enhancing salient object detection through diffusion-based techniques. For reliably diffusing the energy from labeled seeds, we propose a novel graph-based diffusion scheme called affinity learning-based diffusion (ALD), which is based on learning full-range affinity between two arbitrary graph nodes. The method differs from the previous existing work where implicit diffusion was formulated as a ranking problem on a graph. In the proposed method, the affinity learning is achieved in a unified graph-based semi-supervised manner, whose outcome is leveraged for global propagation. By properly selecting an affinity learning model, the proposed ALD outperforms the ranking-based diffusion in terms of accurately detecting salient objects and enhancing the correct salient objects under a range of background scenarios. By utilizing the ALD, we propose an enhanced saliency detector that outperfomis 7 recent state-of-the-art saliency models on 3 benchmark datasets.
The establishment of robust target appearance model over time is an overriding concern in visual tracking. In this paper, we propose an inverse nonnegative matrix factorization (NMF) method for robust appearance model...
详细信息
The establishment of robust target appearance model over time is an overriding concern in visual tracking. In this paper, we propose an inverse nonnegative matrix factorization (NMF) method for robust appearance modeling. Rather than using a linear combination of nonnegative basis vectors for each target image patch in conventional NMF, the proposed method is a reverse thought to conventional NMF tracker. It utilizes both the foreground and background information, and imposes a local coordinate constraint, where the basis matrix is sparse matrix from the linear combination of candidates with corresponding nonnegative coefficient vectors. Inverse NMF is used as a feature encoder, where the resulting coefficient vectors are fed into a SVM classifier for separating the target from the background. The proposed method is tested on several videos and compared with seven state-of-the-art methods. Our results have provided further support to the effectiveness and robustness of the proposed method.
Pulsar candidate selection identifies prospective observations of modern radio pulsar surveys for further inspection in search of real pulsars. Typically, human experts visually select valuable candidates and eliminat...
详细信息
ISBN:
(纸本)9781509048410
Pulsar candidate selection identifies prospective observations of modern radio pulsar surveys for further inspection in search of real pulsars. Typically, human experts visually select valuable candidates and eliminate radio frequency interference or other noises. Recently, machine learning methods are adopted to automate this task, which saves human labor and makes it possible for processing millions of observations efficiently. Considering the number of positive training samples are relatively too small and the cost of incorrectly labeling a real pulsar candidate as negative is large, we propose a novel hierarchical candidate-sifting model by emphasizing the cost of incorrect prediction of positive samples and assembling multiple classifiers trained with different weighting parameters. Experiments on three pulsar selection datasets demonstrate our proposed method improves the pulsar-sifting performance a lot according to several standard evaluation metrics.
This paper presents an approach to derive critical points of a shape, the basis of a Reeb graph, using a combination of a medial axis skeleton and features along this skeleton. A Reeb graph captures the topology of a ...
详细信息
暂无评论